classificationErrorDistance {partitionComparison}

Classification Error Distance

Description

Compute the classification error distance

1 - \frac{1}{n} \max_{\sigma}{\sum_{C \in \cal{P}}{|C \cap \sigma(C)|}}

with \sigma a weighted matching between the clusters of both partitions. The nodes are the classes of each partition, the weights are the overlap of objects.

Usage

classificationErrorDistance(p, q)

## S4 method for signature 'Partition,Partition'
classificationErrorDistance(p, q)

Arguments

p

The partition P

q

The partition Q

Methods (by class)

• classificationErrorDistance(p = Partition, q = Partition): Compute given two partitions

Hint

This measure is implemented using lp.assign from the lpSolve package to compute the maxmimal matching of a weighted bipartite graph.

Author(s)

Fabian Ball [e-mail]

References

Meila M, Heckerman D (2001). “An Experimental Comparison of Model-Based Clustering Methods.” Machine Learning, 42(1), 9–29.

Meila M (2005). “Comparing Clusterings: An Axiomatic View.” In Proceedings of the 22nd International Conference on Machine Learning, ICML '05, 577–584. ISBN 978-1-59593-180-1, doi:10.1145/1102351.1102424.

Examples

isTRUE(all.equal(classificationErrorDistance(new("Partition", c(0, 0, 0, 1, 1)), 
                                             new("Partition", c(0, 0, 1, 1, 1))), 0.2))